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Particulate Matter Sampling Techniques and Data Modelling Methods

In: Air Quality - Measurement and Modeling

Author

Listed:
  • Jacqueline Whalley
  • Sara Zandi

Abstract

Particulate matter with 10 ?m or less in diameter (PM10) is known to have adverse effects on human health and the environment. For countries committed to reducing PM10 emissions, it is essential to have models that accurately estimate and predict PM10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM10 is presented. This includes the instrumentation used to measure particulate matter, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM10 trends in New Zealand--a remote country of islands in the South Pacific Ocean--are examined. In conclusion, the issues and challenges faced when modelling PM10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM10 prediction and estimation models are presented.

Suggested Citation

  • Jacqueline Whalley & Sara Zandi, 2016. "Particulate Matter Sampling Techniques and Data Modelling Methods," Chapters, in: Philip John Sallis (ed.), Air Quality - Measurement and Modeling, IntechOpen.
    • Handle: RePEc:ito:pchaps:107567
    DOI: 10.5772/65054
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    File URL: https://www.intechopen.com/chapters/52206
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    More about this item

    Keywords

    particulate matter; modelling; regression; artificial neural networks; instrumentation and measurement;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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